Introduction

This is a personal Rmarkdown document I have created to visualize the COVID-19 updates and some preliminary exploratory data analysis (EDA). The source of this data is the github repository created and maintained by the Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU).

suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(forecast))
suppressPackageStartupMessages(library(zoo))
suppressPackageStartupMessages(library(xts))
suppressPackageStartupMessages(library(gridExtra))
suppressPackageStartupMessages(library(gghighlight))
suppressPackageStartupMessages(library(knitr))
suppressPackageStartupMessages(library(directlabels))
suppressPackageStartupMessages(library(scales))
suppressPackageStartupMessages(library(plotly))
#suppressPackageStartupMessages(library(rjson))

Reading the data

COVID_confirmed_global_raw <- read_csv("csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv") 
COVID_deaths_global_raw <-  read_csv("csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv") 
COVID_recovered_global_raw <-  read_csv("csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv")

Reshaping and formatting data

# Reshape to longer format
COVID_confirmed_global_longer <- COVID_confirmed_global_raw %>% 
  pivot_longer(cols      = c('1/22/20':names(COVID_confirmed_global_raw)[ncol(COVID_confirmed_global_raw)]),
               names_to  = "date",
               values_to = "n_cases")
  

COVID_deaths_global_longer <- COVID_deaths_global_raw %>% 
  pivot_longer(cols      = c('1/22/20':names(COVID_deaths_global_raw)[ncol(COVID_deaths_global_raw)]),
               names_to  = "date",
               values_to = "n_cases")

COVID_recovered_global_longer <- COVID_recovered_global_raw %>% 
  pivot_longer(cols      = c('1/22/20':names(COVID_recovered_global_raw)[ncol(COVID_recovered_global_raw)]),
               names_to  = "date",
               values_to = "n_cases")


# change column names
colnames(COVID_confirmed_global_longer) <-  c('state', 'country', 'lat', 'long','date', 'n_cases')
colnames(COVID_deaths_global_longer) <-  c('state', 'country', 'lat', 'long','date', 'n_cases')
colnames(COVID_recovered_global_longer) <-  c('state', 'country', 'lat', 'long','date', 'n_cases')

# drop `state` column and create a `new_cases` column
COVID_confirmed_global_longer <- COVID_confirmed_global_longer %>% 
               select(-state)%>% 
               group_by(country, date) %>% 
               summarize(n_cases = sum(n_cases))  
COVID_deaths_global_longer <- COVID_deaths_global_longer %>% 
               select(-state)%>% 
               group_by(country, date) %>% 
               summarize(n_cases = sum(n_cases)) 
COVID_recovered_global_longer <- COVID_recovered_global_longer %>% 
               select(-state) %>% 
               group_by(country, date) %>% 
               summarize(n_cases = sum(n_cases)) 

# convert date columns from character to date format
COVID_confirmed_global_longer$date <- as.Date(COVID_confirmed_global_longer$date, format = '%m/%d/%Y')
COVID_deaths_global_longer$date <- as.Date(COVID_deaths_global_longer$date, format = '%m/%d/%Y')
COVID_recovered_global_longer$date <- as.Date(COVID_recovered_global_longer$date, format = '%m/%d/%Y')

COVID_confirmed_global_longer <- COVID_confirmed_global_longer %>% 
  arrange(country, date) %>% 
  mutate(new_cases = n_cases-lag(n_cases, default = 0))

COVID_deaths_global_longer <- COVID_deaths_global_longer %>% 
  arrange(country, date) %>% 
  mutate(new_cases = n_cases-lag(n_cases, default = 0))

COVID_recovered_global_longer <- COVID_recovered_global_longer %>% 
  arrange(country, date) %>% 
  mutate(new_cases = n_cases-lag(n_cases, default = 0))

Let’s look at the current data format

knitr::kable(head(COVID_confirmed_global_longer),format = 'markdown')
country date n_cases new_cases
Afghanistan 0020-01-22 0 0
Afghanistan 0020-01-23 0 0
Afghanistan 0020-01-24 0 0
Afghanistan 0020-01-25 0 0
Afghanistan 0020-01-26 0 0
Afghanistan 0020-01-27 0 0

creating some functions for quick stats

world_summary <- function() {
   
  df1 <- COVID_confirmed_global_longer %>% 
    group_by(country) %>% 
    summarize(n_cases_today = max(n_cases),
              new_cases_today = dplyr::last(new_cases)) %>%
    ungroup() %>% 
    summarize(n_cases_total = sum(n_cases_today),
              new_cases_total = sum(new_cases_today))
  
  df2 <- COVID_deaths_global_longer %>% 
    group_by(country) %>% 
    summarize(n_cases_today = max(n_cases),
              new_cases_today = dplyr::last(new_cases)) %>% 
    ungroup() %>% 
    summarize(n_cases_total = sum(n_cases_today),
              new_cases_total = sum(new_cases_today))
  
  df3 <- COVID_recovered_global_longer %>%
    group_by(country) %>% 
    summarize(n_cases_today = max(n_cases),
              new_cases_today = dplyr::last(new_cases)) %>% 
    ungroup() %>% 
    summarize(n_cases_total = sum(n_cases_today),
              new_cases_total = sum(new_cases_today))
  
  print(paste0("number of total confirmed cases in the world as of today:  ", df1$n_cases_total, " with ", df1$new_cases_total, " new cases"))
  print(paste0("number of total deaths in the world  as of today:  ", df2$n_cases_total, " with ", df2$new_cases_total, " new deaths"))
  print(paste0("number of total recovered cases in the world  as of today:  ", df3$n_cases_total, " with ", df3$new_cases_total, " new cases"))
  
}


country_summary <- function(country1) {
  
  df1 <- COVID_confirmed_global_longer %>% group_by(country) %>% dplyr::filter(country==country1) %>%  summarize(n_cases_today = max(n_cases),
              new_cases_today = dplyr::last(new_cases))
  df2 <- COVID_deaths_global_longer %>% group_by(country)%>% dplyr::filter(country==country1) %>% summarize(n_cases_today = max(n_cases),
              new_cases_today = dplyr::last(new_cases))
  df3 <- COVID_recovered_global_longer %>% group_by(country)%>% dplyr::filter(country==country1) %>% summarize(n_cases_today = max(n_cases),
              new_cases_today = dplyr::last(new_cases))
  #                                                            
  print(paste0("number of confirmed cases in ", country1, " as of today:  ", df1$n_cases_today, " with ", df1$new_cases_today, " new cases"))
  # df1$n_cases_today

  print(paste0("number of deaths in ", country1, " as of today:  ", df2$n_cases_today, " with ", df2$new_cases_today, " new deaths"))
  # df2$n_cases_today

  print(paste0("number of recovered cases in ", country1, " as of today:  ", df3$n_cases_today, " with ", df3$new_cases_today, " new cases"))
  # df3$n_cases_today
  
}


world_summary()
## [1] "number of total confirmed cases in the world as of today:  1982716 with 58872 new cases"
## [1] "number of total deaths in the world  as of today:  125941 with 6457 new deaths"
## [1] "number of total recovered cases in the world  as of today:  474612 with 25606 new cases"
country_summary("US")
## [1] "number of confirmed cases in US as of today:  607670 with 27051 new cases"
## [1] "number of deaths in US as of today:  25787 with 2258 new deaths"
## [1] "number of recovered cases in US as of today:  47763 with 4281 new cases"
country_summary("Italy")
## [1] "number of confirmed cases in Italy as of today:  162488 with 2972 new cases"
## [1] "number of deaths in Italy as of today:  21067 with 602 new deaths"
## [1] "number of recovered cases in Italy as of today:  37130 with 1695 new cases"
country_summary("Spain")
## [1] "number of confirmed cases in Spain as of today:  172541 with 2442 new cases"
## [1] "number of deaths in Spain as of today:  18056 with 300 new deaths"
## [1] "number of recovered cases in Spain as of today:  67504 with 2777 new cases"
country_summary("China")
## [1] "number of confirmed cases in China as of today:  83306 with 93 new cases"
## [1] "number of deaths in China as of today:  3345 with 0 new deaths"
## [1] "number of recovered cases in China as of today:  78200 with 161 new cases"
country_summary("Egypt")
## [1] "number of confirmed cases in Egypt as of today:  2350 with 160 new cases"
## [1] "number of deaths in Egypt as of today:  178 with 14 new deaths"
## [1] "number of recovered cases in Egypt as of today:  589 with 0 new cases"
country_summary("Germany")
## [1] "number of confirmed cases in Germany as of today:  131359 with 1287 new cases"
## [1] "number of deaths in Germany as of today:  3294 with 100 new deaths"
## [1] "number of recovered cases in Germany as of today:  68200 with 3900 new cases"
country_summary("France")
## [1] "number of confirmed cases in France as of today:  137875 with -6514 new cases"
## [1] "number of deaths in France as of today:  15748 with 762 new deaths"
## [1] "number of recovered cases in France as of today:  29098 with 1097 new cases"

Donut plot

df <- COVID_confirmed_global_longer %>% mutate(country_sum = ifelse(n_cases > 5000, country,"other"))
df <- df %>% group_by(country_sum)
df <- df %>% summarize(count = max(n_cases))
fig <- df %>% plot_ly(labels = ~country_sum, values = ~count, text = ~country_sum)
fig <- fig %>% add_pie(hole = 0.4)
fig <- fig %>% layout(title = "Confirmed cases worldwide",  showlegend = F,
                      xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
                      yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))

fig

Multi-dimensional bar-chart

COVID_confirmed_global_longer %>%
  group_by(country) %>%
  plot_ly(x = ~date, y = ~n_cases, color = ~country) %>%
  add_bars(text = ~country)%>%
  layout(barmode = "stack",
         showlegend = FALSE) 
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

A function for plotting COVID-19 cases for different countries

plot_countries <- function(df, curve_title, cumulative=TRUE, ...) {
  df1 <- df %>% 
    dplyr::filter(country %in% list(...))
  
  if (cumulative) {
  p1 = ggplot(df1, aes(date, n_cases, group=country, color=country))+
    geom_line()+
    scale_x_date(date_breaks = "3 days")+
    scale_y_log10(labels = function(x) format(x, scientific = FALSE),
                  name = "number of cases",
                  breaks = scales::breaks_log(n = 10))+
    theme_bw()+
    theme(axis.text.x = element_text(angle = 90), legend.position = "none")+
    ggtitle(curve_title)+
      geom_dl(data = df1, aes(label = country), method = list(dl.combine("first.points", "last.points"), cex = 0.8))
  } else{
  p1 = ggplot(df1, aes(date, new_cases, group=country, color=country))+
    geom_line()+
    scale_x_date(date_breaks = "3 days")+
    scale_y_log10(labels = function(x) format(x, scientific = FALSE),
                  name = "number of cases",
                  breaks = scales::breaks_log(n = 10))+
    theme_bw()+
    theme(axis.text.x = element_text(angle = 90), legend.position = "none")+
    ggtitle(curve_title)+
      geom_dl(data = df1, aes(label = country), method = list(dl.combine("first.points", "last.points"), cex = 0.8))
  }
  

    
  return(p1)
}

plot_countries(COVID_confirmed_global_longer, curve_title = "Confirmed cases (cumulative)", cumulative = TRUE, "US", "Italy", "Canada", "Egypt",  "china")

plot_countries(COVID_deaths_global_longer, curve_title = "Death cases (cumulative)", cumulative = TRUE,"US", "Italy", "Canada", "Egypt",  "china")

plot_countries(COVID_recovered_global_longer, curve_title = "Recovered cases (cumulative)",cumulative = TRUE, "china","US", "Italy", "Canada", "Egypt",  "china")

plot_countries(COVID_confirmed_global_longer, curve_title = "New confirmed cases", cumulative = FALSE,"US", "Italy", "Canada", "Egypt",  "china")

plot_countries(COVID_deaths_global_longer, curve_title = "New death cases", cumulative = FALSE,"US", "Italy", "Canada", "Egypt",  "china")

plot_countries(COVID_recovered_global_longer, curve_title = "New recovered cases", cumulative = FALSE,"US", "Italy", "Canada", "Egypt",  "china")

Visualizing confirmed cases with new cases

Inspired by this minuteearth video. The thing about this visualization is that it doesn’t plot the Cumulative number of confirmed cases with time, instead with the number of new cases on a log-scale, which is more intuitive. Multiple comparisons between countries with very different number of cases could be very made very clear, and it is very easy to detect whether things are getting better.

COVID_confirmed_smoothed <- COVID_confirmed_global_longer %>% 
  tidyr::nest(-country) %>% 
  dplyr::mutate(m = purrr::map(data, loess,
                                               formula = new_cases ~ n_cases, span = 0.4),
                fitted = purrr::map(m, `[[`, "fitted"))

COVID_confirmed_smoothed <- COVID_confirmed_smoothed %>%
  dplyr::select(-m) %>%
  tidyr::unnest()

COVID_confirmed_smoothed2 <- COVID_confirmed_smoothed %>% 
  dplyr::filter(country %in% c("US", "China", "Italy", "Korea, South", "Iran", "Egypt", "Spain", "Germany", "France", "United Kingdom", "Canada"))

ggplot(data = COVID_confirmed_smoothed2, aes(n_cases, fitted))+
  geom_path(data = COVID_confirmed_smoothed2,aes(n_cases,fitted,color = country, group = country))+
  theme_bw()+
  ylab("number of cases")+
  scale_y_log10(labels = function(x) format(x, scientific = FALSE))+
  scale_x_log10(labels = function(x) format(x, scientific = FALSE))+
  geom_dl(data = COVID_confirmed_smoothed2, aes(label = country), method = list(dl.combine("first.points", "last.points"), cex = 0.8))+
  xlab(label = "Total confirmed cases")+
  ylab(label = "number of new cases")+
  theme(legend.position="none")